Therefore, some directions need to be taken to minimize the latency in FLchain implementation in edge computing.
For example, an FL architecture is proposed in [1]} to accelerate the convergence speed of the FL training, by performing resource allocation in a fashion more resources should be allocated to devices with worse channel conditions or weaker computation capabilities. In this way, devices can achieve a balance in AI training power, thus improving the model aggregation rate for low-latency FL training. Moreover, light-weight mining designs should be focused, by reducing complexity in transaction formulation and block verification stages while security and scalability are preserved [2]}.
Moreover, to optimize the wireless communication latency caused by the model data offloading in the FLchain training, AI techniques such as DRL would be very useful to build latency control policies. For example, a DRL-based approach provided in [3]} can help client devices learn the optimal offloading rule with respect to update sizes, transmit power, and blockchain transaction states, aiming to minimize the communication delays while user privacy is ensured. Another DRL-based approach is also proposed in [4]} to schedule the sub-channel assignment and transmission power control for improving the wireless data transmission rates. In this context, both the strict reliability and latency requirements of all devices are taken into account in a multi-agent setting where mobile devices collaborate to optimize the transmission latency based on local observation information.
